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Drilling Down Newsletter # 15 - December 2001 - Customer LifeCycle

Drilling Down - Turning Customer
Data into Profits with a Spreadsheet
*************************
Customer Valuation, Retention, 
Loyalty, Defection

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Now also available online through
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Prior Newsletters:
http://www.jimnovo.com/newsletters.htm
-------------------------------
Drilling Down Newsletter # 15 - 
December 2001

In this issue:
#  Visitor Quality / Engagement
     Calculator for WebTrends
#  Best of the Best
     Customer Marketing Articles
# Tracking the Customer LifeCycle:
    Results of Poll
# Questions from Fellow Drillers
-------------------------------

Hi again folks, Jim Novo here.  This month we have a free tool for you to help analyze and manage web site visitor quality, followed by the usual "must read" articles on customer Retention from around the web.  The votes are in on the editorial content of the newsletter for the beginning of next year, and I clean up the e-mail bag with a year end rush of questions from fellow Drillers walking the High  ROI Customer Marketing walk.

Sound good?  Let's do some Drillin'!

Visitor Quality / Engagement
Calculator for WebTrends
====================

Every web site has at least two big issues to deal with: generating targeted traffic and making sure this traffic gets "hooked" when it arrives at the site.  Server log analysis using WebTrends can create the raw data you need to track important metrics surrounding these issues of targeted traffic and engaging the visitor, but the raw data isn't very useful in the WebTrends format for these tasks.

This calculator takes the raw data input by a user from the standard WebTrends report and turns this data into actionable metrics you can track over time.  What do I mean by actionable?  All the metrics created by the calculator tell you specific things about the way your visitors are behaving, and you can literally "take action" based on the metrics, tracking the changes in visitor behavior your action caused.  These are not "Gee, that's interesting" metrics.  They are the ones that have dramatic impact on the profitability of your web site, and by tracking them and trying to affect them, you can get your web site a lot closer to the goals you are trying to achieve.

So, as my holiday gift to you, may I present the beta version of the Visitor Quality / Engagement Calculator for WebTrends!  This special edition of the calculator is being provided with detailed background material on how the metrics are derived and how they are used.  Future free editions may not include these details.  Look for more on this next year in association with the good folks at Future Now Inc, who have a few nifty calculators of their own you can download.

The Visitor Quality / Visitor Engagement calculator for WebTrends (Excel spreadsheet format, unzipped or zipped) download is here.

If you have not already, why don't you download the first 4 Chapters of the Drilling Down book, or the ReadMe file for the customer scoring software that comes with the book while you are on the download page!

Best of the Best Customer Retention Articles
====================

I find for this newsletter only one "must read" article they are about to lock away in the paid archives at DM News, but it's a doozy.  So included are 2 other must read articles (which don't expire) you may have missed from elsewhere around the web. 

The URL's are too long for the newsletter, so the links take you to a page with more info on what is in the article and a direct link.

Note to web site visitors: These links may 
have expired by the time you read this.  You
can get these "must read" links e-mailed to
you every 2 weeks before they expire by subscribing to the newsletter.

Beyond Traditional Segmentation - DM News Read by: Expires December 12, 2001 
Just an absolutely fantastic article on life after RFM, which confirms my experience - 80% of the predictive value of the most sophisticated customer behavior model you can develop is available using RFM models created with an Excel spreadsheet.

Measurement of Customer Satisfaction
is Necessary, but What does it Have to do
with Customer Relationships?

November 9, 2001  CRMForum.com
Gets the longest title award, but still a very good debunking of the customer satisfaction survey myth.

Comdex 2001: 
The first steps of Web analytics

November 14, 2001  searchCRM.com
I shouldn't have to bring this up anymore by now folks, but I am absolutely stunned by the number of people who do not use easy, cheap, High ROI web site analytics.  I don't understand how you would run a web business - any web business - without them.

Make sure you check out the calculator mentioned above if you are having difficulty deciding what is important to track or how to use a WebTrends report!

Tracking the Customer LifeCycle: Latency
=====================

Speaking of stunned, about 26% of you voted on the choice to continue with the Latency topic or not, and 99% of those voted for continuation.  Amazing.  Who am I to argue?  Continue we will, but will put it off and start with a fresh perspective next year.  We will continue to Track the Customer LifeCycle, with a focus on how to use the LifeCycle to make more money in your marketing efforts.

If you are new to the group or want to review the first five parts of the series, click here.

Questions from Fellow Drillers
=====================

Q:  Just wondered if you could answer a question I can't ever seem to find an adequate answer for.

A: I'll give it a shot.  I have a pretty good track record so far...

Q: Are customer loyalty and customer retention the same thing, the terms are used so interchangeably with one another, I presume they're not - so how do they differ and conversely how are they similar?

A: They can be the same, in a broad sense.  If you don't have customer retention, you don't have customer loyalty, and vice versa.  I think most "old guys" like me think of customer retention as the very tactical and targeted to individual customer actions you take to keep customers on board.  "Loyalty" is the end result of these programs. 

Personally, I don't think any customers are "loyal."  They may be loyal at a point in time, but it seems to me this is more like "infatuation."  It isn't loyalty, which to me implies a long-term affair.  Your best friend is "loyal."  Harley Hog buyers are "loyal," and Harley Davidson is one of the very few companies that can claim loyal customers in the true sense of the word.

For most companies, they will be lucky if they can get "retention" - a short term, tactical idea; never mind loyalty  - a long term, emotional idea.  While we're at it, let's throw in "customer satisfaction."  This is the weakest sister; customers can be satisfied and neither "retained" nor "loyal."  The fact that some pretty famous "experts" use these words interchangeably tells you they really have no practical knowledge of consumer behavior.

Q: Also, what's the best way to measure customer retention - as customer satisfaction surveys will never provide a good measure?

A: Retention is really easy to measure if you have direct contact with customers.  There's a ton of stuff about it on my web site - in fact, that's just about all my web site is about.  The fact you didn't get this message is somewhat troubling to me.  Did you read the tutorials on Latency and Recency?  Both are excellent ways to measure customer retention.  See:

Tutorial: Latency
Tutorial: Recency

If these are too "difficult" for you at this time, try the Recent  Repeaters model.

If you don't have direct contact with customers, well, that's another story completely.  I'd have to know more about what industry you are in and the role you play in that industry.  Describe your situation and perhaps I can help. 

As far as satisfaction surveys, they can be used as a proxy for retention if you create a hard behavioral linkage between the two.  For example, do your satisfaction survey, and then track the retention rates of the actual people in the survey.  If you find a hard match between satisfaction and retention, then satisfaction = retention, simple as that.  You want to recheck this kind of proxy at the very least each time you have a major change in product, service, marketing, and so forth.  At the high end of confidence, if you repeated this matching of satisfaction and retention every year, you could be highly confident it holds true over time.

Hope this answers your question, and if you need additional direction, please let me know.
-------------------------------

Q:  I read your section about how "R" and "F" are better indicators than "M" which I agree. But for the problem I face, do you have any ideas on how I can redefine "F" for my purpose?  If not, I can always use RM, but will face the drawbacks you mentioned in the book which I think are legitimate concerns for predicting potential value. 

(Jim's note: this Driller is referring to the modified RFM model used in the Drilling Down book.  For an overview of what he is talking about see this description of what is in the book and this outline of RFM.)

A: Just to ground this discussion, I assume you are talking about (a major enterprise software company with many products).

You should look for R and F in other places, if "short term" prediction is what you are after  (I'll discuss long term in a minute).  Long cycle businesses like enterprise software can be more difficult to model because the variables you are looking to do an RF scoring on are not as obvious.  The sales activity may not be particularly predictive of customer behavior because the nature of the business precludes frequency of purchase.

For example, think customer service.  Where in your organization would you see RF show up relative to customer satisfaction?  Perhaps at the call center, help desk, or "outstanding issue" logs of the implementation team?  There could certainly be other areas, depending on how customer care is set up.  The question is: how does the Recency and Frequency of customer care predict the likelihood of customer defection?

Despite the fact you sell a "product," one could imagine you are really in the service business. This type of product sets up (hopefully) a very long Customer LifeCycle and ongoing service relationship with upgrades, add-ons, customization, and so forth.  Perhaps most of the profit is really in the ongoing relationship, not the initial sale.  If true, this is where the focus on RF profiling should be.

You want to go where the transactional behavior is, because this transactional behavior is predictive.  So you have to find out where it is and run your profiling there.  For example, once the installation is over (is it ever over?), what is the Recency and Frequency of calls for assistance?  Does the RF of "trouble calls" predict the likelihood of additional sales in the future, or is it a negative predictor - the higher the score, the less likely a customer is to upgrade?  Many times in a service business, high RF scores indicate negative satisfaction, as you probably can imagine.

Somewhere in the organization there is transactional data predictive of likelihood to buy additional services / likelihood to defect.  Your mission (should you choose to accept it) is to figure out where it is, or if it does not exist, create a way to capture it.

Now long term.  Over very long Customer LifeCycles, one simply has to extend the time horizon. Remember, RF is a relative, not absolute, scoring system, which is why it is useful across such a broad range of businesses.  It compares and ranks activity between customers, not against an external benchmark.  So even though "frequency" may be every 5 or 10 years, it is still predictive relative to other customers.

For example (and I certainly don't know your business, so I am making this up as I go) say there is a "base" package, an ERP Accounting / Planning / Forecasting module.  It's the product you are well known for and has high customer satisfaction; the product most companies buy first when they engage in a relationship with you.

Let's say satisfied, best customers tend to add on to this base module as the years go by.  They add Human Resources, Warehouse Control, CRM, e-business marketplaces,. etc.  This may happen every 3 -5 years.  But some customers do it more quickly the others, and this is where you see high RF scores, as compared with others who do it more slowly.  So you still get an RF ranking, and you still get predictive power in the model, even though the transactions are spread out over decades.  Your challenge may simply be this - you don't have data that goes back over decades.

What you want to know is this: once you have identified high scoring customers, what is it about them that is similar?  Is it who made the initial sale, the type of business they are in, geography?  If you compare high scoring and low scoring customers, what are the differences?  What kind of business adds on to the base module every 2 years as opposed to the kind of business that adds on every 5?

Plus, can you use this knowledge to predict defection, or in your case, a low likelihood of further upgrades?  If the top 20% best (most profitable) customer businesses make their first add-on by year 3 after the initial install of the base module, what does it mean when a business passes by year 3 and does not add on?  Is this a red flag?  Should you send in a "specialist" to find out why the add-on has not happened?  Are they experiencing problems with the base module which were never documented, or worse, never fixed?  Setting up this kind of "early warning system" can be very helpful in a customer retention effort - the behavior of the customer is telling you, flashing a signal, that something is wrong relative to other customers.

I hope the above answers your question.  Long cycle B2B is not as simple to profile as B2C, but the behavior is still there.  You just have to look a little harder for it.  Here's some additional resources on my site.

The first goes deeper into behavior
profiling for service-oriented businesses.

The second reviews Latency, first cousin to Recency and another "early warning system" metric which for some organizations is easier than Recency to "sell" internally and implement.  The "didn't add-on by year 3" example above is a form of Latency tracking.

Good luck with it!  Let me know if you have
further questions.
-------------------------------

Q: I've been reading your web page and I'm interested in your book.  I do have a question about your model.  Measuring repeaters and Recency make a lot of sense.  These are the customers that we should monitor.  If this # (percent) starts to decrease, how do you know if this behavior is due to being "unhappy" w/ your company or if this behavior can be explained by current economic activity?  When things are tight, I might have to buy less frequently....not because I defected as a customer, but rather because that is all I can afford this buying cycle? 

A: It sounds like you were reading the Recent Repeaters model, one of the most basic models on the site.  And you are correct, when you cannot isolate a variable and have multiple effects happening at the same time, you can't rely on any model to tell you what is going on.  The essence of modeling is screening out noise so what you are measuring can be attributed to a single source. If you change all your product offerings and redesign your site at the same time, and customer loyalty (% Recent Repeaters) drops, you will never know for sure if this was because of the new products or the new design.

That said, the drop is still real and tangible, whether caused by a weak economy, displeasure with the company, or another variable.  At least if you are tracking Recent Repeaters, you can predict a future drop in business - whatever the cause.  That capability by itself would be quite valuable.  

Did you see the more complex (but still easy to implement) models on the site?  They are covered in the two tutorials:

Latency - "Trip Wire Marketing"
Recency - "Predicting Customer Value"

These two are generally more powerful than Recent Repeaters, and in both cases, are about making more money - regardless of what the economic situation.  The first deals with recognizing and attacking customer defection.  The second is about comparing the potential value of new customers generated by various sources - ads, products, search engines, etc.  The Recency Model is a lot closer to what is actually in the book.  If you want to see a Chapter by Chapter overview of the book contents spelling out exactly what is in there, see this page.

Hope this answered your question, and
good luck to you!
===================

That's it for this month's edition of the Drilling Down newsletter.  If you like the newsletter, please forward it to a friend!  Subscription instructions are top and bottom of this page.

---------------------------
If you're in a tight spot on a customer marketing program or CRM initiative (it just doesn't pay out / can't prove it makes money) and need some help making it profitable, check out my project-oriented services
------------------------------ 

Any comments on the newsletter (it's too long, too short, topic suggestions, etc.) please send them right along to me, along with any other questions on customer Valuation, Retention, Loyalty, and Defection here.

'Til next time, keep Drilling Down!

- Jim Novo

Copyright 2001, The Drilling Down Project by Jim Novo.  All rights reserved.  You are free to use material from this newsletter in whole or in part as long as you include complete attribution, including live web site link and/or e-mail link. Please tell me where & when the material will appear. 

 

 
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